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BackgroundMany journals now require authors share their data with other investigators, either by depositing the data in a public repository or making it freely available upon request. These policies are explicit, but remain largely untested. We sought to determine how well authors comply with such policies by requesting data from authors who had published in one of two journals with clear data sharing policies.Methods and FindingsWe requested data from ten investigators who had published in either PLoS Medicine or PLoS Clinical Trials. All responses were carefully documented. In the event that we were refused data, we reminded authors of the journal's data sharing guidelines. If we did not receive a response to our initial request, a second request was made. Following the ten requests for raw data, three investigators did not respond, four authors responded and refused to share their data, two email addresses were no longer valid, and one author requested further details. A reminder of PLoS's explicit requirement that authors share data did not change the reply from the four authors who initially refused. Only one author sent an original data set.ConclusionsWe received only one of ten raw data sets requested. This suggests that journal policies requiring data sharing do not lead to authors making their data sets available to independent investigators.

Background There is little consensus on a standard approach to analysing bone scan images. The Bone Scan Index (BSI) is predictive of survival in patients with progressive prostate cancer (PCa), but the popularity of this metric is hampered by the tedium of the manual calculation. Objective Develop a fully automated method of quantifying the BSI and determining the clinical value of automated BSI measurements beyond conventional clinical and pathologic features. Design, setting, and participants We conditioned a computer-assisted diagnosis system identifying metastatic lesions on a bone scan to automatically compute BSI measurements. A training group of 795 bone scans was used in the conditioning process. Independent validation of the method used bone scans obtained ≤3 mo from diagnosis of 384 PCa cases in two large population-based cohorts. An experienced analyser (blinded to case identity, prior BSI, and outcome) scored the BSI measurements twice. We measured prediction of outcome using pretreatment Gleason score, clinical stage, and prostate-specific antigen with models that also incorporated either manual or automated BSI measurements. Measurements The agreement between methods was evaluated using Pearson’s correlation coefficient. Discrimination between prognostic models was assessed using the concordance index (C-index). Results and limitations Manual and automated BSI measurements were strongly correlated (ρ = 0.80), correlated more closely (ρ = 0.93) when excluding cases with BSI scores ≥10 (1.8%), and were independently associated with PCa death (p < 0.0001 for each) when added to the prediction model. Predictive accuracy of the base model (C-index: 0.768; 95% confidence interval [CI], 0.702–0.837) increased to 0.794 (95% CI, 0.727–0.860) by adding manual BSI scoring, and increased to 0.825 (95% CI, 0.754–0.881) by adding automated BSI scoring to the base model. Conclusions Automated BSI scoring, with its 100% reproducibility, reduces turnaround time, eliminates operator-dependent subjectivity, and provides important clinical information comparable to that of manual BSI scoring.

Background-We previously reported the learning curve for open radical prostatectomy, defined in terms of prostate cancer recurrence. We sought to characterize the learning curve for laparoscopic radical prostatectomy.

PurposeWe previously reported that a panel of four kallikrein forms in blood—total, free, and intact prostate-specific antigen (PSA) and kallikrein-related peptidase 2 (hK2)—can reduce unnecessary biopsy in previously unscreened men with elevated total PSA. We aimed to replicate our findings in a large, independent, representative, population-based cohort.Patients and MethodsThe study cohort included 2,914 previously unscreened men undergoing biopsy as a result of elevated PSA (≥ 3 ng/mL) in the European Randomized Study of Screening for Prostate Cancer, Rotterdam, with 807 prostate cancers (28%) detected. The cohort was randomly divided 1:3 into a training and validation set. Levels of kallikrein markers were compared with biopsy outcome.ResultsAddition of free PSA, intact PSA, and hK2 to a model containing total PSA and age improved the area under the curve from 0.64 to 0.76 and 0.70 to 0.78 for models without and with digital rectal examination results, respectively (P < .001 for both). Application of the panel to 1,000 men with elevated PSA would reduce the number of biopsies by 513 and miss 54 of 177 low-grade cancers and 12 of 100 high-grade cancers. Findings were robust to sensitivity analysis.ConclusionWe have replicated our previously published finding that a panel of four kallikreins can predict the result of biopsy for prostate cancer in men with elevated PSA. Use of this panel would dramatically reduce biopsy rates. A small number of men with cancer would be advised against immediate biopsy, but these men would have predominately low-stage, low-grade disease.